Build a Secure Local AI System (LLM, RAG & AI Agents)

Budget: 6000 UAH Deadline: 14 days

Hello Anastasia,

Here is how I would build it, and where I would push back on the spec.

Proposed stack: Ollama serving a quantised open model (Qwen2.5 or Mistral, 7-14B depending on your hardware), Qdrant as the vector store, LlamaIndex for the RAG layer, FastAPI for the API, all in Docker Compose behind Nginx. Ingestion: PyMuPDF for PDF with Tesseract OCR only on pages that actually need it (OCR over everything is the usual reason these pipelines crawl), python-docx and openpyxl for DOCX/XLSX, Whisper for transcripts. Dedup by content hash, versioning by document id plus revision, so a re-uploaded file replaces its old chunks instead of doubling them.

Two points that matter more than the framework choice. Citations must be returned as data - chunk ids and source file resolved from the retrieval step, never requested of the model in the prompt. A model told to cite will invent citations; a pipeline that returns them cannot. And client isolation belongs in the Qdrant query filter, enforced per tenant, not in application code where one missed branch leaks another client's documents.

Timeline and cost, milestone-based as you asked:

M1, 14 days, the bid amount: LLM running on your server, ingestion for PDF/OCR/DOCX/TXT/XLSX, Qdrant index with dedup and versioning, FastAPI query endpoint returning cited sources, Docker deployment and setup docs.
M2: meeting agents - transcripts, summaries, action-item extraction - plus the web query interface.
M3: auth and roles, per-client isolation, security validation, performance tuning.

Full M1-M3 estimate 22000-28000 UAH. I prefer fixed price per milestone over hourly, so you get a working system at the end of each one, not a timesheet.

Straight about the portfolio you asked for: our paid client work is production Python and API automation on Linux VPS - a Google Ads API reporting tool, Telegram bots for live businesses, mail infrastructure. The local-LLM, RAG and multi-agent side I run in-house daily, a self-hosted agent system with retrieval over its own document base. I can show that live on a screen-share, so you see retrieval and citations working before committing anything.

One question that decides the architecture: what CPU, RAM and GPU does the target server have? A 7B model on CPU alone answers in 10-20 seconds; on a 16GB GPU, under two. With no GPU I would size the model to your box and tell you what hardware is worth buying, rather than promise speed it cannot deliver.

Petro Pankov, BotCraft Group Expand

Budget: 18000 UAH Deadline: 14 days

Hello Anastasia,
The main risk in this project is not connecting a local language model to a vector database. It is ensuring that document access restrictions, source citations, document versions, and tenant isolation continue to work correctly throughout ingestion, retrieval, and agent execution.
I can build a complete self-hosted first production version of the platform using Python, FastAPI, Docker Compose, a local LLM runtime, Qdrant or an equivalent vector database, and LlamaIndex where it provides a clear advantage.
The delivered system will include:
Local LLM deployment on your VPS or dedicated server.
Document ingestion for PDF, OCR pages, DOCX, TXT, XLSX, and meeting transcripts.
Content hashing, metadata, deduplication, document revisions, and re-indexing.
RAG answers with source file and chunk references returned by the retrieval layer.
Meeting transcript processing, summaries, action items, and client knowledge extraction.
FastAPI endpoints and a simple web interface for document upload and knowledge queries.
Authentication, roles, and isolated document collections for separate clients or departments.
Docker deployment, logging, backups, tests, and written setup documentation.
My practical background includes Python development, databases, REST APIs, automation, AI API integrations, Telegram systems, and connecting business software with custom applications. I will not claim a completed public multi-tenant RAG case that I cannot prove.
Within the first 72 hours, I will deliver the architecture, threat and data-flow map, Docker skeleton, and a working local prototype that imports one document and returns an answer with a verified source reference.
Fixed price: 18,000 UAH. Delivery term: 14 calendar days. The server, GPU hardware, hosting fees, and paid third-party licenses are not included. Work and approvals can be handled in writing through the Freelancehunt workspace. Expand

Budget: 6000 UAH Deadline: 2 days

Hello, I am ready to design and assemble a secure local AI system with LLM, RAG, and agents tailored to your business processes. I have experience with Python backend, Node.js bots, n8n, and Hermes; for this task, I consider Hermes the most relevant option. On Hermes, an agent can be created so that you can later edit the logic, instructions, and behavior through a chat in Telegram. Implementation: I will select a local model, configure RAG based on your data, manage access controls, and assemble basic agent automation scenarios. If you need an API or an admin layer around the system, I will create it using FastAPI or Node.js. git: github.com/onyx144

Budget: 1000 UAH Deadline: 15 days

Partially the same as with the previous project. Here’s what I can offer:

Hello! I reviewed the project.

I can handle part of it through n8n + OpenAI:

— RAG logic via OpenAI Assistants API with document uploads (PDF, DOCX, TXT) — no local LLM, but fast and reliable
- Agents for processing meeting transcripts → automatic summaries + task lists
- Document upload pipeline and queries to the knowledge base
- Simple web interface or Telegram bot for queries
- API endpoint via n8n webhook

Honestly about the limitations: local LLM on VPS, vector databases (Qdrant/Chroma), and Python architecture are outside my stack. If having a self-hosted model is critical - I am not the right candidate.

If an OpenAI-based solution with full privacy through the Assistants API works for you - we can discuss the details. Expand

Budget: 24000 UAH Deadline: 50 days

Hi Anastasia,

I build this stack. My own project is a desktop AI client in Rust/Tauri — four LLM provider protocols behind one interface, native tool-calling loop, local RAG on sqlite-vec with hybrid keyword+semantic retrieval, and a corrective-RAG graph in a Python sidecar. Hand-written, no LangChain. github.com/kamedashe

Before I bid properly — is 700 UAH the real budget or a placeholder? For the scope you describe it reads like a placeholder, and I'd rather ask than guess.

And to be straight about the boundary: I do the application layer — indexing, retrieval, citations, agents, the API around them. I have not run GPU infrastructure for a company. If self-hosting the model itself is in scope, that's a separate piece.

Bohdan

Budget: 700 UAH Deadline: 3 days

Hello, Anastasia.
I have worked on similar projects — I know the nuances and will do exactly what is described. There are a few clarifications before starting.
The final price/timeframe will be determined after all clarifications.

Profile: Freelancehunt
Reviews: Freelancehunt

Budget: 700 UAH Deadline: 7 days

Good day, Anastasia!

I have experience in developing secure AI systems, including local LLMs and RAG, which allows for the creation of an effective platform for automating business processes.

I understand the importance of data security and confidentiality for your project. I have experience with technologies that ensure reliable data isolation and access control.

To create an effective strategy, I suggest first familiarizing myself with your requirements and project specifications. After that, I will be able to prepare an action plan, including development and implementation stages.

The preliminary cost of my services will be from $400 per month. The duration of the project will depend on its complexity, but typically, implementing similar solutions takes from 2 to 4 months.

In my portfolio, you will find examples of similar projects that confirm my expertise.

I suggest discussing the details of your project in private messages to determine the next steps and goals. Expand

Budget: 20000 UAH Deadline: 90 days

i already have a nearly ready similar self-hosted ai and rag solution we can adapt and launch quickly...i am online and can discuss the first milestone here now ))

for the complete production scope the fixed estimate is usd 20000 and about 90 days

the listed 700 uah cannot cover this scope...a safe first milestone is architecture and a working private prototype for usd 2400 in 5 working days

should deployment support one company or separate isolated client organizations?

what server hardware and expected document volume are available?

https://business.ingello.com/en/fractal - multi-agent orchestration and automated engineering workflows

https://business.ingello.com/en/vorfahr - a production ai and saas system with business automation

https://systems-fl.ingello.com/en - our ai automation and custom software practice Expand

Budget: 700 UAH Deadline: 14 days

Anastasia, it sounds like you need a robust, self-hosted architecture that keeps your company data off third-party clouds while maintaining high-quality responses via RAG. To make this work, I will focus on setting up a stable containerized environment with a local LLM, integrating LlamaIndex for precise document retrieval, and defining the specific workflows for your agents to handle transcripts and internal docs effectively. Could you clarify what hardware resources you have available for the server and how many concurrent users will be accessing the system daily?